Building AI from chess - data shape from simulation
Not an expert here by any means. I think the simplest way to approach the chess problem is to have three components a) a game logic component, that can generate all the possible legal moves from a given board position b) an evaluation function that can score a given game position and c) your minimax/alphabeta algorithm that searches through the tree of possible moves generated by a) looking for the branch that returns the best score using b). Here, the machine learning part would be the evaluation function. You would just transform each board position in your data into a feature vector, and train some kind of classifier to return the probability of win/lose at that given board position. I think it's highly doubtful that this would work with such a small data set though.